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identity, and gender expression), marital family, and parental status, pregnancy, genetic information, military enlistment, or veteran status, and any other class of individuals protected from discrimination
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What You’ll Need: PhD in computer science, artificial intelligence, machine learning, computational biology, biomedical engineering, or a closely related quantitative field. Strong foundation in modern
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 1 month ago
processing, computer programming, and fieldwork are encouraged to apply. The successful candidate will be a member of the Geophysics Department based at RSES. RSES is Australia’s leading academic research
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of Visual Intelligence is to develop novel, innovative solutions based on deep learning to extract knowledge from complex image data. Deep learning, aided by machine learning techniques in general, has led
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across the following research areas: Predictive machine learning Robust and stochastic optimization Learning-enabled control and reinforcement learning Power system operations, planning, and electricity
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, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an ‘exciting place to learn and grow. We welcome you to join our community
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that solve big problems. We support research that universities, companies, and venture capital firms don’t fund because they view it as too risky. We prefer to use the word “challenging,” and we love
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the value of the data. Role Summary Design and conduct large-scale laboratory and field trials aimed at underground infrastructure Use classical geophysical sensors (e.g. GPR, gravimeter, magnetometer) as
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The University of British Columbia (UBC) | Vancouver UBC, British Columbia | Canada | about 1 month ago
government-solicited reports. In 2024, CEDRN executed new Membership and Data Sharing Agreements across urban, rural, and remote EDs, enabling large-scale analyses and new surveillance initiatives. Building
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laboratory analytical methods (e.g., chromatography, mass spectrometry). Familiarity with AI or machine learning applications relevant to environmental data analysis. Basic knowledge of GIS/mapping tools